Surface Roughness Analysis of Carbon/Glass Hybrid Polymer Composites in Drilling Process Based on Taguchi and Response Surface Methodology

2015 ◽  
Vol 1119 ◽  
pp. 622-627 ◽  
Author(s):  
Chye Lih Tan ◽  
Azwan Iskandar Azmi ◽  
Noorhafiza Muhammad
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Regression Model ◽  
Response Surface ◽  
Feed Rate ◽  
Hybrid Composite ◽  
Spindle Speed ◽  
Tool Geometry ◽  
Secondary Process ◽  
Drilling Process

Drilling is an essential secondary process for near net-shape of hybrid composite as to achieve the required dimensional tolerances prior to final application. Dimensional tolerance is often influenced by the surface integrity or surface roughness of the workpart. Thus, this paper aims to employ the Taguchi and response surface methodologies in minimizing the surface roughness of drilled carbon-glass hybrid fibre reinforced polymer (CGCG) using tungsten carbide, K20 drill bits. The effects of spindle speed, feed rate and tool geometry on surface roughness were evaluated and optimum cutting conditions for minimizing the aforementioned response was determined. Subsequently, response surface methodology (RSM) was utilised in finding the empirical relationships between experimental parameters and surface roughness based on the Taguchi results. The experimental analyses reveal that surface roughness is greatly influenced by feed rate and tool geometry rather than the spindle speed. This is due to the increment of feed that attributed to the increased strain rate and hence, deteriorated the surface roughness of the hybrid composite. The predicted results (via regression model) and theoretical results (via additivity law) were in good agreement with experiment results. This indicates that the regression model from response surface methodology (RSM) can be used to predict the surface roughness in machining of CGCG hybrid composite.

scholarly journals Surface Roughness Optimization in Drilling Process Using Response Surface Method (RSM)

2014 ◽  
Vol 66 (3) ◽  
Author(s):  
Mohd Amran ◽  
Siti Salmah ◽  
Mohd Sanusi ◽  
Mohd Yuhazri ◽  
Noraiham Mohamad ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Surface Roughness ◽  
Response Surface ◽  
Response Surface Method ◽  
Feed Rate ◽  
Spindle Speed ◽  
Drilling Process ◽  
Surface Method ◽  
Drilling Parameters ◽  
Drill Diameter

This paper presents the effect of drilling parameters on surface roughness and surface appearance by applying response surface method (RSM). The mathematical model for correlating the interactions of drilling parameters such as spindle speed, feed rate and drill diameter on surface roughness was developed. RSM methodology was used as it is a technique that most practical and effective way to develop a mathematical model. In addition, this method also can reduce trial and error in experiment. Since the number of factors are three; spindle speed, feed rate and drill diameter, by applying RSM the total numbers of experiment involved are 20 experimental observations. From the experimental result, it is found that the minimum surface roughness on the hole was 1.06 mm from combination of 2000 rpm spindle speed, 78 mm/min feed rate and 2.5 mm drill diameter. While the maximum surface roughness 2.59 mm was the combination of 250 rpm spindle speed, 153 mm/min feed rate and 3.5 mm drill diameter. A mathematical equation was developed with percentage of error are 0% to 29%. Thus, from the result we understand that to find the smooth surface in drilling process, it needs higher spindle speed with lower feed rate and smaller diameter.

Optimization of Cutting Parameters during Turning of AISI-310 Using Response Surface Methodology

2016 ◽  
Vol 854 ◽  
pp. 45-51
Author(s):  
S. Nandhakumar ◽  
R. Vijayakumar ◽  
Senthil Padmavathy ◽  
N. Nagasundaram
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Feed Rate ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Parameters Optimization ◽  
Depth Of Cut ◽  
Optimization Of Cutting Parameters

Design of Experiments is employed to study the stimulus of cutting parameters such as feed rate, spindle speed, depth of cut in the turning operation of AISI-310 and optimizing the value of those parameters for getting the higher material removal rate (MRR) and minimal surface roughness. A prediction model has been developed by using the above influencing parameters. For the purpose of parameters optimization we investigate the parameters using Response Surface Methodology (RSM). It is shown that feed rate is the main parameter in influencing the surface roughness, which is being followed by spindle speed and depth of cut. It is found that surface roughness and feed rate were directly proportional to each other for some extent. The confirmation tests were carried out to with the optimum set of parameters and are verified with test results. The comparison of above two results were found to be good with maximum error within 5% on comparing it with the predicted model.

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

scholarly journals Effect of Cutting Parameters and Tool Geometry on the Performance Analysis of One-Shot Drilling Process of AA2024-T3

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 854
Author(s):  
Muhammad Aamir ◽  
Khaled Giasin ◽  
Majid Tolouei-Rad ◽  
Israr Ud Din ◽  
Muhammad Imran Hanif ◽  
...  
Keyword(s):  
Feed Rate ◽  
Surface Defects ◽  
Thrust Force ◽  
Cutting Tools ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Machining Process ◽  
Tool Geometry ◽  
Drilling Process

Drilling is an important machining process in various manufacturing industries. High-quality holes are possible with the proper selection of tools and cutting parameters. This study investigates the effect of spindle speed, feed rate, and drill diameter on the generated thrust force, the formation of chips, post-machining tool condition, and hole quality. The hole surface defects and the top and bottom edge conditions were also investigated using scan electron microscopy. The drilling tests were carried out on AA2024-T3 alloy under a dry drilling environment using 6 and 10 mm uncoated carbide tools. Analysis of Variance was employed to further evaluate the influence of the input parameters on the analysed outputs. The results show that the thrust force was highly influenced by feed rate and drill size. The high spindle speed resulted in higher surface roughness, while the increase in the feed rate produced more burrs around the edges of the holes. Additionally, the burrs formed at the exit side of holes were larger than those formed at the entry side. The high drill size resulted in greater chip thickness and an increased built-up edge on the cutting tools.

Parameter Optimization of Ball End Milling Process on Inconel 718 Using RSM and TLBO Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 293-300 ◽  
Author(s):  
Nandkumar N. Bhopale ◽  
Nilesh Nikam ◽  
Raju S. Pawade
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Integrity ◽  
Inconel 718 ◽  
Tool Path ◽  
End Milling ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Ball End Milling

AbstractThis paper presents the application of Response Surface Methodology (RSM) coupled with Teaching Learning Based Optimization Technique (TLBO) for optimizing surface integrity of thin cantilever type Inconel 718 workpiece in ball end milling. The machining and tool related parameters like spindle speed, milling feed, axial depth of cut and tool path orientation are optimized with considerations of multiple response like deflection, surface roughness, and micro hardness of plate. Mathematical relationship between process parameters and deflection, surface roughness and microhardness are found out by using response surface methodology. It is observed that after optimizing the process that at the spindle speed of 2,000 rpm, feed 0.05 mm/tooth/rev, plate thickness of 5.5 mm and 15° workpiece inclination with horizontal tool path gives favorable surface integrity.

Optimization of Surface Roughness in Turning of Ti-6Al-4V Using Response Surface Methodology and TLBO

2015 ◽  
Author(s):  
Neelesh Ku. Sahu ◽  
A. B. Andhare
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Titanium Alloys ◽  
Response Surface ◽  
Feed Rate ◽  
Cutting Speed ◽  
Maximum Error ◽  
Depth Of Cut ◽  
Interaction Variables ◽  
Teaching Learning

Surface roughness is an important surface integrity parameter for difficult to cut alloys such as Titanium alloys (Ti-6Al-4V). In the present work, initially a mathematical model is developed for predicting surface roughness for turning operation using Response Surface Methodology (RSM). Later, a recently developed advanced optimization algorithm named as Teaching Learning Based Optimization (TLBO) is used for further parameter optimization of the equation developed using RSM. The design of experiments was performed using central composite design (CCD). Analysis of variance (ANOVA) demonstrated the significant and non-significant parameters as well as validity of predicted model. RSM describes the effect of main and mixed (interaction) variables on the surface roughness of titanium alloys. RSM analysis over experimental results showed that surface roughness decreased as cutting speed increased whereas it increased with increase in feed rate. Depth of cut had no effect on surface roughness. By comparing the predicted and measured values of surface roughness the maximum error was found to be 7.447 %. It indicates that the developed model can be effectively used to predict the surface roughness. Further optimization of the roughness equation was carried out by TLBO method. It gave minimum surface roughness as 0.3120 μm at the cutting speed of 1704 RPM (171.217 m/min), feed rate of 55.6 mm/min (.033 mm/rev) and depth of cut of 0.7 mm. These results were confirmed by confirmation experiment and were better than that of RSM.

Investigations on Surface Roughness Measurement in Minimum Quantity Lubrication Turning of Titanium Alloys Using Response Surface Methodology and Box–Cox Transformation

2016 ◽  
Vol 16 (2) ◽  
pp. 75-88 ◽  
Author(s):  
Munish Kumar Gupta ◽  
P. K. Sood ◽  
Vishal S. Sharma
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Flow Rate ◽  
Feed Rate ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Machining Parameters ◽  
Minimum Quantity ◽  
Box Cox Transformation

AbstractIn the present work, an attempt has been made to establish the accurate surface roughness (Ra, Rq and Rz) prediction model using response surface methodology with Box–Cox transformation in turning of Titanium (Grade-II) under minimum quantity lubrication (MQL) conditions. This surface roughness model has been developed in terms of machining parameters such as cutting speed, feed rate and approach angle. Firstly, some experiments are designed and conducted to determine the optimal MQL parameters of lubricant flow rate, input pressure and compressed air flow rate. After analyzing the MQL parameter, the final experiments are performed with cubic boron nitride (CBN) tool to optimize the machining parameters for surface roughness values i. e., Ra, Rq and Rz using desirability analysis. The outcomes demonstrate that the feed rate is the most influencing factor in the surface roughness values as compared to cutting speed and approach angle. The predicted results are fairly close to experimental values and hence, the developed models using Box-Cox transformation can be used for prediction satisfactorily.

scholarly journals Effect of Cutting Parameter on Material Removal Rate and Surface Roughness in Deep Drilling Process

2016 ◽  
Vol 2 (5) ◽  
Author(s):  
Mostafa A. Abdullah  , Ahmed B. Abdulwahhab   ,   Atheer R.
Keyword(s):  
Surface Roughness ◽  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Spindle Speed ◽  
Cutting Conditions ◽  
Drilling Process ◽  
Tool Diameter ◽  
Twist Drills

In the curents study aimed to assess the effects of cutting conditions  (spindle speed, feed rate, tool diameter) parameters as input impact on material removal rate (MRR) and surface roughness (Ra) as output of steel (AISI 1015). A number of drilling experiments were conducted using the L9 orthogonal array on conventional drilling machine with use feed rate (0.038,0.076,0.203) mm/rev and spindle speed (132,550,930) rpm and tool diameter (11,15,20) mm HSS twist drills under dry cutting conditions. Analysis of variance (ANOVA) was employed to determine the most significant control factors affecting on surface roughness and MRR. The result shown the tool diameter the important factor effect with (64.08%) and (76.12%) on MRR and surface roughness respectively.

Multi-Objective Optimization Using Box-Behken of Response Surface Methodology for High-Speed Machining of Inconel 718

2014 ◽  
Vol 629 ◽  
pp. 487-492 ◽  
Author(s):  
Mohd Shahir Kasim ◽  
Che Hassan Che Haron ◽  
Jaharah Abd Ghani ◽  
E. Mohamad ◽  
Raja Izamshah ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Tool Life ◽  
Feed Rate ◽  
Inconel 718 ◽  
High Speed ◽  
Cutting Speed ◽  
High Speed Milling

This study was carried out to investigate how the high-speed milling of Inconel 718 using ball nose end mill could enhance the productivity and quality of the finish parts. The experimental work was carried out through Response Surface Methodology via Box-Behnken design. The effect of prominent milling parameters, namely cutting speed, feed rate, depth of cut (DOC), and width of cut (WOC) were studied to evaluate their effects on tool life, surface roughness and cutting force. In this study, the cutting speed, feed rate, DOC, and WOC were in the range of 100 - 140 m/min, 0.1 - 0.2 mm/tooth, 0.5 - 1.0 mm and 0.2 - 1.8 mm, respectively. In order to reduce the effect of heat generated during the high speed milling operation, minimum quantity lubrication of 50 ml/hr was used. The effect of input factors on the responds was identified by mean of ANOVA. The response of tool life, surface roughness and cutting force together with calculated material removal rate were then simultaneously optimized and further described by perturbation graph. Interaction between WOC with other factors was found to be the most dominating factor of all responds. The optimum cutting parameter which obtained the longest tool life of 60 mins, minimum surface roughness of 0.262 μm and resultant force of 221 N was at cutting speed of 100 m/min, feed rate of 0.15 mm/tooth, DOC 0.5 m and WOC 0.66 mm.

scholarly journals Modeling and Analysis for Surface roughness in Machining EN-31 steel using Response Surface Methodology

2011 ◽  
pp. 33-38
Author(s):  
L B Abhang ◽  
M Hameedullah
Keyword(s):  
Surface Roughness ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Confidence Level ◽  
Feed Rate ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Cutting Velocity ◽  
En 31 ◽  
Tool Nose Radius

This paper utilizes the regression modeling in turning process of En-31 steel using response surface methodology (RSM) with factorial design of experiments. A first-order and second-order surface roughness predicting models were developed by using the experimental data and analysis of the relationship between the cutting conditions and response (surface roughness). In the development of predictive models, cutting parameters of cutting velocity, feed rate, depth of cut, tool nose radius and concentration of lubricants were considered as model variables, surface roughness were considered as response variable. Further, the analysis of variance (ANOVA) was used to analyze the influence of process parameters and their interaction during machining. From the analysis, it is observed that feed rate is the most significant factor on the surface roughness followed by cutting speed and depth of cut at 95% confidence level. Tool nose radius and concentration of lubricants seem to be statistically less significant at 95% confidence level. Furthermore, the interaction of cutting velocity/feed rate, cutting velocity/ nose radius and depth of cut/nose radius were found to be statistically significant on the surface finish because their p-values are smaller than 5%. The predicted surface roughness values of the samples have been found to lie close to that of the experimentally observed values.

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